Although it has not yet been satisfactorily defined, the problem of defensive medicine has received much recent attention. One aspect of defensive medicine is the practice of ordering diagnostic tests and monitoring procedures solely to reduce the chances of being sued. Although few data are available, the costs of such practices are thought to be high. This study is designed to provide an objective index of the pressures promoting defensive medicine in 15 medical and surgical specialties. The research will focus on malpractice claims associated with omission of diagnostic tests and physiologic monitoring; such claims are conceptualized as-the driving force behind many defensive medical practices. Using existing data from the files of the largest physician malpractice insurance carrier in New Jersey, the proposed research aims to: (1) determine the proportion of claims associated with diagnostic and monitoring omissions in 15 specialties, (2) compare (within specialty) indemnity payments and severity of patient injuries for diagnostic/monitoring omissions vs. other kinds of claims, (3) describe the particular diagnostic/monitoring omissions commonly encountered in each specialty, (4) determine whether the proportion of claims ascribed to diagnostic/monitoring omissions has changed with time and/or the introduction of new technologies, and (5) assess whether physicians' demographic and practice characteristics and/or the characteristics of the hospitals where they practice can predict their propensity to incur claims for diagnostic or monitoring omissions. Malpractice claims filed with the carrier between 1977 and 1989 will be sorted by specialty and screened for the occurrence of diagnostic or monitoring omissions. The proportion of claims associated with such omissions will be calculated by specialty, with separate calculations for claims filed but not pursued, claims pursued but not paid or otherwise ascribed to negligence, and claims paid or judged by peers as resulting from negligence. The association of physician and hospital characteristics with omission-related claims will be assessed using categorical analysis, multivariate regression, and recursive partitioning (classification trees).